Background Melanoma is one of the most life-threatening skin cancers; immune checkpoint blockade is widely used in the treatment of melanoma because of its remarkable efficacy. Objective This study aimed to conduct a comprehensive bibliometric analysis of research conducted in recent decades on immune checkpoint blockade for melanoma, while exploring research trends and public interest in this topic. Methods We summarized the articles in the Web of Science Core Collection on immune checkpoint blockade for melanoma in each year from 1999 to 2020. The R package bibliometrix was used for data extraction and visualization of the distribution of publication year and the top 10 core authors. Keyword citation burst analysis and cocitation networks were calculated with CiteSpace. A Gunn online world map was used to evaluate distribution by country and region. Ranking was performed using the Standard Competition Ranking method. Coauthorship analysis and co-occurrence were analyzed and visualized with VOSviewer. Results After removing duplicates, a total of 9169 publications were included. The distribution of publications by year showed that the number of publications rose sharply from 2015 onwards and either reached a peak in 2020 or has yet to reach a peak. The geographical distribution indicated that there was a large gap between the number of publications in the United States and other countries. The coauthorship analysis showed that the 149 top institutions were grouped into 8 clusters, each covering approximately a single country, suggesting that international cooperation among institutions should be strengthened. The core author extraction revealed changes in the most prolific authors. The keyword analysis revealed clustering and top citation bursts. The cocitation analysis of references from 2010 to 2020 revealed the number of citations and the centrality of the top articles. Conclusions This study revealed trends in research and public interest in immune checkpoint blockade for melanoma. Our findings suggest that the field is growing rapidly, has several core authors, and that the United States is taking the lead position. Moreover, cooperation between countries should be strengthened, and future research hot spots might focus on deeper exploration of drug mechanisms, prediction of treatment efficacy, prediction of adverse events, and new modes of administration, such as combination therapy, which may pave the way for further research.

Background Skin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited. Objective This study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases. Methods Each image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences. Results Xiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%). Conclusions Xiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.

Background Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. Objective This study aimed to develop an image–artificial intelligence (AI)–based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. Methods A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. Results The proposed image-AI–based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. Conclusions An image-AI–based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists’ accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Targeted liposomes, as a promising carrier, have received tremendous attention in COVID‐19 vaccines, molecular imaging, and cancer treatment, due to their enhanced cellular uptake and payload accumulation at target sites. However, the conventional methods for preparing targeted liposomes still suffer from limitations, including complex operation, time‐consuming, and poor reproducibility. Herein, a facile and scalable strategy is developed for one‐step construction of targeted liposomes using a versatile microfluidic mixing device (MMD). The engineered MMD provides an advanced synthesis platform for multifunctional liposome with high production rate and controllability. To validate the method, a programmed death‐ligand 1 (PD‐L1)‐targeting aptamer modified indocyanine green (ICG)‐liposome (Apt‐ICG@Lip) is successfully constructed via the MMD. ICG and the PD‐L1‐targeting aptamer are used as model drug and targeting moiety, respectively. The Apt‐ICG@Lip has high encapsulation efficiency (89.9 ± 1.4%) and small mean diameter (129.16 ± 5.48 nm). In vivo studies (PD‐L1‐expressing tumor models) show that Apt‐ICG@Lip can realize PD‐L1 targeted photoacoustic imaging, fluorescence imaging, and photothermal therapy. To verify the versatility of this approach, various targeted liposomes with different functions are further prepared and investigated. These experimental results demonstrate that this method is concise, efficient, and scalable to prepare multifunctional targeted liposomal nanoplatforms for molecular imaging and disease theranostics.

Abstract:Our aim was to analyze the effect of headgear on upper airway dimension and hyoid bone position on non-extraction patients with Class II division 1 malocclusion. Ninety patients with Class II division 1 malocclusion were included and divided into three groups (Group A: treated with headgears and Class II traction; Group B: performed with Class II traction; and Group C: no treatment). The lateral projection was measured at the beginning and end of treatment. Cephalometric analyses of the dentofacial structure, upper airway dimension and hyoid bone position were performed before and after treatment. The data were analyzed by paired t-test and independent sample t-test. SNA significantly decreased in Group A after treatment (P<0.01), and SNB, ANB and Wits significantly changed in Group A and Group B. L1-MP, L1-NB and Z angle significantly increased, while overjet, overbite and lower lip measurements significantly decreased in Group A and Group B. In upper airway measurements, V-LPW, PNS-V, PNS-U and T-V significantly increased for three groups. Moreover, the hyoid bone position had a change in Group A and Group B. The upper airway dimensions were not decreased by headgear treatment or Class II traction in patients with Class II division 1 malocclusion. Class II traction treatment affected the position of the hyoid bone.